• Journal of IKEEE
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• v.25 no.3
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• pp.493-500
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• 2021

In this paper, we propose the development of deep learning structure to improve quality of polygonal containers. The deep learning structure consists of a convolution layer, a bottleneck layer, a fully connect layer, and a softmax layer. The convolution layer is a layer that obtains a feature image by performing a convolution 3x3 operation on the input image or the feature image of the previous layer with several feature filters. The bottleneck layer selects only the optimal features among the features on the feature image extracted through the convolution layer, reduces the channel to a convolution 1x1 ReLU, and performs a convolution 3x3 ReLU. The global average pooling operation performed after going through the bottleneck layer reduces the size of the feature image by selecting only the optimal features among the features of the feature image extracted through the convolution layer. The fully connect layer outputs the output data through 6 fully connect layers. The softmax layer multiplies and multiplies the value between the value of the input layer node and the target node to be calculated, and converts it into a value between 0 and 1 through an activation function. After the learning is completed, the recognition process classifies non-circular glass bottles by performing image acquisition using a camera, measuring position detection, and non-circular glass bottle classification using deep learning as in the learning process. In order to evaluate the performance of the deep learning structure to improve quality of polygonal containers, as a result of an experiment at an authorized testing institute, it was calculated to be at the same level as the world's highest level with 99% good/defective discrimination accuracy. Inspection time averaged 1.7 seconds, which was calculated within the operating time standards of production processes using non-circular machine vision systems. Therefore, the effectiveness of the performance of the deep learning structure to improve quality of polygonal containers proposed in this paper was proven.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.3
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• pp.204-212
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• 2019

The optimal application rate of a controlled release fertilizer (CRF) on the growth, yield, and seeding time of rice grown on seedling trays was investigated. The experimental field was located at $37^{\circ}22^{\prime}10^{{\prime}{\prime}}N$ latitude and $127^{\circ}03^{\prime}85^{{\prime}{\prime}}E$ longitude in Hwaseong, Gyeonggi-do, Republic of Korea. The soil in the paddy field was a clay loam. The CRF used in the experiment contained $300g\;kg^{-1}$ of nitrogen, $60g\;kg^{-1}$ of phosphate, and $60g\;kg^{-1}$ of potassium, respectively. The CRF was applied at the rate of 0, 200, 300, 400, 500, and 600 grams on rice seedling tray compared with the field application based on soil testing (control), respectively. The CRF can be applied as single application(which can replace basal fertilizer application and two top dressing application) directly to the seedling tray, and showed the minimum release at the seedling period. Considering the plant growth, nitrogen use efficency and yield of rice, the optimal application rate of developed CRF was 500 g per seedling tray and the yield of rice at this application rate was $4.92{\sim}5.04Mg\;ha^{-1}$. The regression formula between the rice yield and application rates of CRF was as follows ; "$Y=0.0002{\chi}^2+0.0963{\chi}+411.6$($R^2$ : 0.9922) in 2010 and $Y=8E-6{\chi}^2+0.2723{\chi}+344.04$($R^2$:0.9864) in 2011, Y : Rice yield ($Mg\;ha^{-1}$), ${\chi}$ : Application rate (grams) of controlled release fertilizer". The optimum application rates of CRF per rice seedling tray by regression formula was 498 grams in 2010 and 513 grams in 2011.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.109-131
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• 2014

As the demand of nuclear power plant equipment is continuously growing worldwide, the importance of handling nuclear strategic materials is also increasing. While the number of cases submitted for the exports of nuclear-power commodity and technology is dramatically increasing, preadjudication (or prescreening to be simple) of strategic materials has been done so far by experts of a long-time experience and extensive field knowledge. However, there is severe shortage of experts in this domain, not to mention that it takes a long time to develop an expert. Because human experts must manually evaluate all the documents submitted for export permission, the current practice of nuclear material export is neither time-efficient nor cost-effective. Toward alleviating the problem of relying on costly human experts only, our research proposes a new system designed to help field experts make their decisions more effectively and efficiently. The proposed system is built upon case-based reasoning, which in essence extracts key features from the existing cases, compares the features with the features of a new case, and derives a solution for the new case by referencing similar cases and their solutions. Our research proposes a framework of case-based reasoning system, designs a case-based reasoning system for the control of nuclear material exports, and evaluates the performance of alternative keyword extraction methods (full automatic, full manual, and semi-automatic). A keyword extraction method is an essential component of the case-based reasoning system as it is used to extract key features of the cases. The full automatic method was conducted using TF-IDF, which is a widely used de facto standard method for representative keyword extraction in text mining. TF (Term Frequency) is based on the frequency count of the term within a document, showing how important the term is within a document while IDF (Inverted Document Frequency) is based on the infrequency of the term within a document set, showing how uniquely the term represents the document. The results show that the semi-automatic approach, which is based on the collaboration of machine and human, is the most effective solution regardless of whether the human is a field expert or a student who majors in nuclear engineering. Moreover, we propose a new approach of computing nuclear document similarity along with a new framework of document analysis. The proposed algorithm of nuclear document similarity considers both document-to-document similarity (${\alpha}$) and document-to-nuclear system similarity (${\beta}$), in order to derive the final score (${\gamma}$) for the decision of whether the presented case is of strategic material or not. The final score (${\gamma}$) represents a document similarity between the past cases and the new case. The score is induced by not only exploiting conventional TF-IDF, but utilizing a nuclear system similarity score, which takes the context of nuclear system domain into account. Finally, the system retrieves top-3 documents stored in the case base that are considered as the most similar cases with regard to the new case, and provides them with the degree of credibility. With this final score and the credibility score, it becomes easier for a user to see which documents in the case base are more worthy of looking up so that the user can make a proper decision with relatively lower cost. The evaluation of the system has been conducted by developing a prototype and testing with field data. The system workflows and outcomes have been verified by the field experts. This research is expected to contribute the growth of knowledge service industry by proposing a new system that can effectively reduce the burden of relying on costly human experts for the export control of nuclear materials and that can be considered as a meaningful example of knowledge service application.

• Journal of Distribution Research
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• v.17 no.2
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• pp.63-92
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• 2012

This study investigated consumer intention to use a location-based mobile shopping service (LBMSS) that integrates cognitive and affective responses. Information relevancy was integrated into pleasure-arousal-dominance (PAD) emotional state model in the present study as a conceptual framework. The results of an online survey of 335 mobile phone users in the U.S. indicated the positive effects of arousal and information relevancy on pleasure. In addition, there was a significant relationship between pleasure and intention to use a LBMSS. However, the relationship between dominance and pleasure was not statistically significant. The results of the present study provides insight to retailers and marketers as to what factors they need to consider to implement location-based mobile shopping services to improve their business performance. Extended Abstract : Location aware technology has expanded the marketer's reach by reducing space and time between a consumer's receipt of advertising and purchase, offering real-time information and coupons to consumers in purchasing situations (Dickenger and Kleijnen, 2008; Malhotra and Malhotra, 2009). LBMSS increases the relevancy of SMS marketing by linking advertisements to a user's location (Bamba and Barnes, 2007; Malhotra and Malhotra, 2009). This study investigated consumer intention to use a location-based mobile shopping service (LBMSS) that integrates cognitive and affective response. The purpose of the study was to examine the relationship among information relevancy and affective variables and their effects on intention to use LBMSS. Thus, information relevancy was integrated into pleasure-arousal-dominance (PAD) model and generated the following hypotheses. Hypothesis 1. There will be a positive influence of arousal concerning LBMSS on pleasure in regard to LBMSS. Hypothesis 2. There will be a positive influence of dominance in LBMSS on pleasure in regard to LBMSS. Hypothesis 3. There will be a positive influence of information relevancy on pleasure in regard to LBMSS. Hypothesis 4. There will be a positive influence of pleasure about LBMSS on intention to use LBMSS. E-mail invitations were sent out to a randomly selected sample of three thousand consumers who are older than 18 years old and mobile phone owners, acquired from an independent marketing research company. An online survey technique was employed utilizing Dillman's (2000) online survey method and follow-ups. A total of 335 valid responses were used for the data analysis in the present study. Before the respondents answer any of the questions, they were told to read a document describing LBMSS. The document included definitions and examples of LBMSS provided by various service providers. After that, they were exposed to a scenario describing the participant as taking a saturday shopping trip to a mall and then receiving a short message from the mall. The short message included new product information and coupons for same day use at participating stores. They then completed a questionnaire containing various questions. To assess arousal, dominance, and pleasure, we adapted and modified scales used in the previous studies in the context of location-based mobile shopping service, each of the five items from Mehrabian and Russell (1974). A total of 15 items were measured on a seven-point bipolar scale. To measure information relevancy, four items were borrowed from Mason et al. (1995). Intention to use LBMSS was captured using two items developed by Blackwell, and Miniard (1995) and one items developed by the authors. Data analyses were conducted using SPSS 19.0 and LISREL 8.72. A total of usable 335 data were obtained after deleting the incomplete responses, which results in a response rate of 11.20%. A little over half of the respondents were male (53.9%) and approximately 60% of respondents were married (57.4%). The mean age of the sample was 29.44 years with a range from 19 to 60 years. In terms of the ethnicity there were European Americans (54.5%), Hispanic American (5.3%), African-American (3.6%), and Asian American (2.9%), respectively. The respondents were highly educated; close to 62.5% of participants in the study reported holding a college degree or its equivalent and 14.5% of the participants had graduate degree. The sample represents all income categories: less than $24,999 (10.8%), $25,000-$49,999 (28.34%), $50,000-$74,999 (13.8%), and $75,000 or more (10.23%). The respondents of the study indicated that they were employed in many occupations. Responses came from all 42 states in the U.S. To identify the dimensions of research constructs, Exploratory Factor Analysis (EFA) using a varimax rotation was conducted. As indicated in table 1, these dimensions: arousal, dominance, relevancy, pleasure, and intention to use, suggested by the EFA, explained 82.29% of the total variance with factor loadings ranged from .74 to .89. As a next step, CFA was conducted to validate the dimensions that were identified from the exploratory factor analysis and to further refine the scale. Table 1 exhibits the results of measurement model analysis and revealed a chi-square of 202.13 with degree-of-freedom of 89 (p =.002), GFI of .93, AGFI = .89, CFI of .99, NFI of .98, which indicates of the evidence of a good model fit to the data (Bagozzi and Yi, 1998; Hair et al., 1998). As table 1 shows, reliability was estimated with Cronbach's alpha and composite reliability (CR) for all multi-item scales. All the values met evidence of satisfactory reliability in multi-item measure for alpha (>.91) and CR (>.80). In addition, we tested the convergent validity of the measure using average variance extracted (AVE) by following recommendations from Fornell and Larcker (1981). The AVE values for the model constructs ranged from .74 through .85, which are higher than the threshold suggested by Fornell and Larcker (1981). To examine discriminant validity of the measure, we again followed the recommendations from Fornell and Larcker (1981). The shared variances between constructs were smaller than the AVE of the research constructs and confirm discriminant validity of the measure. The causal model testing was conducted using LISREL 8.72 with a maximum-likelihood estimation method. Table 2 shows the results of the hypotheses testing. The results for the conceptual model revealed good overall fit for the proposed model. Chi-square was 342.00 (df = 92, p =.000), NFI was .97, NNFI was .97, GFI was .89, AGFI was .83, and RMSEA was .08. All paths in the proposed model received significant statistical support except H2. The paths from arousal to pleasure (H1: ${\ss}$=.70; t = 11.44), from information relevancy to intention to use (H3 ${\ss}$ =.12; t = 2.36), from information relevancy to pleasure (H4 ${\ss}$ =.15; t = 2.86), and pleasure to intention to use (H5: ${\ss}$=.54; t = 9.05) were significant. However, the path from dominance to pleasure was not supported. This study investigated consumer intention to use a location-based mobile shopping service (LBMSS) that integrates cognitive and affective responses. Information relevancy was integrated into pleasure-arousal-dominance (PAD) emotional state model as a conceptual framework. The results of the present study support previous studies indicating that emotional responses as well as cognitive responses have a strong impact on accepting new technology. The findings of this study suggest potential marketing strategies to mobile service developers and retailers who are considering the implementation of LBMSS. It would be rewarding to develop location-based mobile services that integrate information relevancy and which cause positive emotional responses.

• Journal of Global Scholars of Marketing Science
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• v.18 no.2
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• pp.89-111
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• 2008

Due to the development of digital technology, studies regarding smart wear integrating daily life have rapidly increased. However, consumer research about perception and attitude toward smart clothing hardly could find. The purpose of this study was to identify innovative characteristics and perceived risk of smart clothing and to analyze the influences of theses factors on product attitudes and intention to adopt. Specifically, five hypotheses were established. H1: Perceived attributes of smart clothing except for complexity would have positive relations to product attitude or purchase intention, while complexity would be opposite. H2: Product attitude would have positive relation to purchase intention. H3: Product attitude would have a mediating effect between perceived attributes and purchase intention. H4: Perceived risks of smart clothing would have negative relations to perceived attributes except for complexity, and positive relations to complexity. H5: Product attitude would have a mediating effect between perceived risks and purchase intention. A self-administered questionnaire was developed based on previous studies. After pretest, the data were collected during September, 2006, from university students in Korea who were relatively sensitive to innovative products. A total of 300 final useful questionnaire were analyzed by SPSS 13.0 program. About 60.3% were male with the mean age of 21.3 years old. About 59.3% reported that they were aware of smart clothing, but only 9 respondents purchased it. The mean of attitudes toward smart clothing and purchase intention was 2.96 (SD=.56) and 2.63 (SD=.65) respectively. Factor analysis using principal components with varimax rotation was conducted to identify perceived attribute and perceived risk dimensions. Perceived attributes of smart wear were categorized into relative advantage (including compatibility), observability (including triability), and complexity. Perceived risks were identified into physical/performance risk, social psychological risk, time loss risk, and economic risk. Regression analysis was conducted to test five hypotheses. Relative advantage and observability were significant predictors of product attitude (adj $R^2$=.223) and purchase intention (adj $R^2$=.221). Complexity showed negative influence on product attitude. Product attitude presented significant relation to purchase intention (adj $R^2$=.692) and partial mediating effect between perceived attributes and purchase intention (adj $R^2$=.698). Therefore hypothesis one to three were accepted. In order to test hypothesis four, four dimensions of perceived risk and demographic variables (age, gender, monthly household income, awareness of smart clothing, and purchase experience) were entered as independent variables in the regression models. Social psychological risk, economic risk, and gender (female) were significant to predict relative advantage (adj $R^2$=.276). When perceived observability was a dependent variable, social psychological risk, time loss risk, physical/performance risk, and age (younger) were significant in order (adj $R^2$=.144). However, physical/performance risk was positively related to observability. The more Koreans seemed to be observable of smart clothing, the more increased the probability of physical harm or performance problems received. Complexity was predicted by product awareness, social psychological risk, economic risk, and purchase experience in order (adj $R^2$=.114). Product awareness was negatively related to complexity, meaning high level of product awareness would reduce complexity of smart clothing. However, purchase experience presented positive relation with complexity. It appears that consumers can perceive high level of complexity when they are actually consuming smart clothing in real life. Risk variables were positively related with complexity. That is, in order to decrease complexity, it is also necessary to consider minimizing anxiety factors about social psychological wound or loss of money. Thus, hypothesis 4 was partially accepted. Finally, in testing hypothesis 5, social psychological risk and economic risk were significant predictors for product attitude (adj $R^2$=.122) and purchase intention (adj $R^2$=.099) respectively. When attitude variable was included with risk variables as independent variables in the regression model to predict purchase intention, only attitude variable was significant (adj $R^2$=.691). Thus attitude variable presented full mediating effect between perceived risks and purchase intention, and hypothesis 5 was accepted. Findings would provide guidelines for fashion and electronic businesses who aim to create and strengthen positive attitude toward smart clothing. Marketers need to consider not only functional feature of smart clothing, but also practical and aesthetic attributes, since appropriateness for social norm or self image would reduce uncertainty of psychological or social risk, which increase relative advantage of smart clothing. Actually social psychological risk was significantly associated to relative advantage. Economic risk is negatively associated with product attitudes as well as purchase intention, suggesting that smart-wear developers have to reflect on price ranges of potential adopters. It will be effective to utilize the findings associated with complexity when marketers in US plan communication strategy.

• Journal of Science and Technology Studies
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• v.6 no.1 s.11
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• pp.119-152
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• 2006

This paper deals with the historical development of research community in Korea. As the former studies of the korean scientific community show, the government supported institutes played an important role in the formation of research community. Therefore the theme of this study is concerned with the historical development of the government supported institutes and the features of their researcher group. In this paper following questions will be answered: How the social status of these researcher group is changed, what kind of response on social problems or national politics they had, and which characteristic they showed with regards to the identity problem. After the korean liberation the government institutes, such as the Chungang Kongop Yonguso(industrial research center)and the Korean Atomic Energy Research Institute, contributed to the development of the first generation of research group. However this research group could hardly identify themselves as researcher, because they spent much time on testing, evaluation or education. The identity problem is also resulted from the deficiency of authority as research institute. The status of researcher had no difference from that of civil servant. With the establishment of KIST the korean research community came into blossom. The government supported institutes, which were founded after the model of KIST, allowed quantitative and qualitative growth of research community. Thanks to the guarantee of institutional authority and the new reward system, the researcher could get respect and improve its social status. During this period the researcher volunteered to help the government policies. We can find often the nationalistic statements in the research community. During 1990s the research group demonstrated different behaviors and attitude toward the government. The nationalistic ideology disappeared. Instead of that, the research group criticized the government policies and took actions against the government. Those changes are related with the lowered position of government supported institutes.

• The Korean Journal of Food And Nutrition
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• v.24 no.4
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• pp.551-558
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• 2011

This present study was performed to analyze the efficiency and volatility of a porous film containing $Chamaecyparis$ $obtusa$ extract as a method to effectively package food compounds. Phytoncide was contacted the state of gas and showed effective antimicrobial properties. Limonene can be distilled without decomposition as a relatively stable terpene and was one of the extract components. $Chamaecyparis$ $obtusa$ essential oil. The optimal solvent composition was a ratio 5:20:0.3 of T-500:ethanol:hardener to effectively manufacture film containing phytoncide essential oil and the minimum antibacterial concentration was 2%. The films were made under different conditions(A-50LF1, A-25SF2, B-50SF1, C-50LF1, C-25SF2 and D-50SF1) containing phytoncide and the amounts of limonene inside the 1-L reaction chamber depending on storage were measured by gas chromatography-mass selective detention. The results showed that the 25SF2(width, 25 mm; length, 20 cm) revealed more amount of limonene compared with 50LF1(width 50 mm, length 20 cm). We confirmed that the gas emission amount showed a better layer on the film side than on the internal film. An effect of film thickness on phytoncide emissions was observed in that the amounts was less than the expectation for a thicker film at the beginning time, but the emitting amounts increased with increasing storage periods. In the storage testing of various films at $35^{\circ}C$ and 70% humidity for 14 days, 25SF2 showed longer preservation compared with that of 50LF in the case of bread. $C.$ $obtusa$ essential oil is a useful fresh ingredients, hence, analysis of limonene emission kinetics from various film was helpful to develop films with an optimal antimicrobial effect, and will allow application of such films in food packaging systems.

• Korean Journal of Clinical Laboratory Science
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• v.49 no.2
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• pp.79-87
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• 2017

GenesWell$^{TM}$ BCT is a 12-gene test suggesting the prognostic risk score (BCT Score) for distant metastasis within the first 10 years in early breast cancer patients with hormone receptor-positive, HER2-negative, and pN0~1 tumors. In this study, we validated the analytical performance of GenesWell$^{TM}$ BCT. Gene expression values were measured by a one-step, real-time qPCR, using RNA extracted from FFPE specimens of early breast cancer patients. Limit of Blank, Limit of Detection, and dynamic range for each of the 12 genes were assessed by serially diluted RNA pools. The analytical precision and specificity were evaluated by three different RNA samples representing low risk group, high risk group, and near-cutoff group in accordance with their BCT Scores. GenesWell$^{TM}$ BCT could detect gene expression of each of the 12 genes from less than $1ng/{\mu}L$ of RNA. Repeatability and reproducibility across multiple testing sites resulted in 100% and 98.3% consistencies of risk classification, respectively. Moreover, it was confirmed that the potential interference substances does not affect the risk classification of the test. The findings demonstrate that GenesWell$^{TM}$ BCT have high analytical performance with over 95% consistency for risk classification.

• Korean journal of applied entomology
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• v.46 no.1 s.145
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• pp.87-95
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• 2007

Many kinds of environment friendly agricultural materials were used for the insect pest control and the control of plant diseases, furthermore they support the growth of crops in the greenhouses and the kindly environment friendly farming. Phytoseiulus persimilis might be used for control of two-spotted mites with environment friendly agricultural materials at the same time. For testing the toxicity of environment friendly agricultural materials against to p. persimilis, 61 environment friendly agricultural materials were selected by material contents and using methods. When environment friendly agricultural materials were directly sprayed on P. persimilis, IEFAM C, FEFAM A, EFAMSM A, D, EFAMPE A, EFAMCh B, EFAMME A, and EFAMMo C killed over 90%. However, there was no effects to FEFAM C, D, EFAMSM C, EFAML A, EFAMME C, E, H, J, EFAMMo G and I against P. persimilis. P. persimilis adults were not survived in vial for 48 hours after sprayed and dried with the environment friendly agricultural materials, fer examples, EFAMSM I, EFAMME A, EFAMMo A, C, and I. Otherwise, EFAMCh C and EFAMMo B were no effects to P. persimilis. Some environment friendly agricultural materials are of different qualities, and consequently test of their foxily have to necessary.